 | MLOps Prod Best Practice In the MLOps ecosystem only about 30% leverage CI/CD or Dev/Staging/Prod environments; only 16% leverage A/B tests, and just about 10% leverage canary deployments. It is important to consider that the Machine Learning Development Lifecycle is still in the early stages of maturity in industry, particularly in production where best practices in software operations at scale are only slowly being adopted into the ML ecosystem. We are uncovering important insights as part of our survey on The State of Production ML in 2024; please contribute to this valuable investigation on machine learning tools and platforms used in your production ML development. Your input will help create a comprehensive overview of common practices, tooling preferences, and challenges faced when deploying models to production, ultimately benefiting the entire ML community 🚀 |
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GenAI Simulating 1k Real People Stanford leveraging GenAI to replicate 1,052 real individuals in a simulation of agents which aims to provide new mechanisms for testing large-scale policymaking and social science. This is quite an interesting area of GenAI exploring how agents can replicate the attitudes and behaviors of the
individuals that they represent, and simulate their interactions in specific test scenarios. These agents were evaluated against their real-world counterparts with professional surveys, performing with 85% accuracy compared to original human responses. This of course opens up important ethical considerations as world-simulations can be created with game-like NPCs replicating real-people behaviour and their interactions - certainly a space to keep a close eye on. |
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Salesforce's Time Series ML Salesforce research enters the Foundation Model race with a time-series forecasting large model, which comes with interesting innovations in model size and optimization: Salesforce introduces Moirai-MoE, a state-of-the-art time series foundation model for universal forecasting which outperforms some of the previously released foundation models. It's great to see development in the forecasting space particularly after the releases of other foundation models from the likes of Google, Amazon, Nixtla, etc - looking forward to seeing the benchmarks as this field continues to evolve. |
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The Data Engineer Handbook A great resource for beginner and experienced data engineers - the Data Engineering Handbook: This is a great compilation of resources in the data engineering space with a clear roadmap for entering the field + a YouTube bootcamp - certainly worth checking out the wealth of blogs, whitepapers, podcasts, newsletters, glossaries, design patterns, courses, and certifications. |
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Anthropic's Responsible AI Anthropic has detailed its approach to responsible AI through risk assessment and mitigation practices across their end to end foundation model lifecycle. Anthropic presents their Responsible Scaling Policy, which outlines: 1) safeguards with model capabilities; 2) conducting rigorous risk assessments; 3) ensuring robust security and privacy measures; 4) contributing to global technical standards; 5) fostering societal impact by partnering with organizations; and 6) addressing trust and safety concerns. It is quite interesting to see organisations publishing their committments to responsible AI; it will now be more interesting to follow how these are implemented in practice. |
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Upcoming MLOps Events The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below. Upcoming conferences where we're speaking:
Other upcoming MLOps conferences in 2024:
In case you missed our talks:
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Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. Four featured libraries in the GPU acceleration space are outlined below. - Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced data processing usecases.
- CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
- Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
- CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
If you know of any open source and open community events that are not listed do give us a heads up so we can add them! |
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As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following: - MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
- AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems.
- An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles.
- ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018.
If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request!
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